Pandas Profiling Report

Dataset statistics

Number of variables	18
Number of observations	115584
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	15.9 MiB
Average record size in memory	144.0 B

Variable types

Categorical	9
Numeric	9

Alerts

`accident_year` has constant value "2020"	Constant
`accident_index` has a high cardinality: 91199 distinct values	High cardinality
`accident_reference` has a high cardinality: 91199 distinct values	High cardinality
`casualty_reference` is highly correlated with `car_passenger`	High correlation
`casualty_class` is highly correlated with `pedestrian_location` and 2 other fields	High correlation
`age_of_casualty` is highly correlated with `age_band_of_casualty`	High correlation
`age_band_of_casualty` is highly correlated with `age_of_casualty`	High correlation
`pedestrian_location` is highly correlated with `casualty_class` and 2 other fields	High correlation
`pedestrian_movement` is highly correlated with `casualty_class` and 2 other fields	High correlation
`car_passenger` is highly correlated with `casualty_reference` and 1 other fields	High correlation
`casualty_type` is highly correlated with `pedestrian_location` and 1 other fields	High correlation
`casualty_home_area_type` is highly correlated with `casualty_imd_decile`	High correlation
`casualty_imd_decile` is highly correlated with `casualty_home_area_type`	High correlation
`casualty_class` is highly correlated with `pedestrian_location` and 1 other fields	High correlation
`age_of_casualty` is highly correlated with `age_band_of_casualty`	High correlation
`age_band_of_casualty` is highly correlated with `age_of_casualty`	High correlation
`pedestrian_location` is highly correlated with `casualty_class` and 1 other fields	High correlation
`pedestrian_movement` is highly correlated with `casualty_class` and 1 other fields	High correlation
`casualty_home_area_type` is highly correlated with `casualty_imd_decile`	High correlation
`casualty_imd_decile` is highly correlated with `casualty_home_area_type`	High correlation
`casualty_class` is highly correlated with `pedestrian_location` and 1 other fields	High correlation
`age_of_casualty` is highly correlated with `age_band_of_casualty`	High correlation
`age_band_of_casualty` is highly correlated with `age_of_casualty`	High correlation
`pedestrian_location` is highly correlated with `casualty_class` and 2 other fields	High correlation
`pedestrian_movement` is highly correlated with `casualty_class` and 2 other fields	High correlation
`casualty_type` is highly correlated with `pedestrian_location` and 1 other fields	High correlation
`sex_of_casualty` is highly correlated with `accident_year`	High correlation
`casualty_severity` is highly correlated with `accident_year`	High correlation
`accident_year` is highly correlated with `sex_of_casualty` and 5 other fields	High correlation
`pedestrian_road_maintenance_worker` is highly correlated with `accident_year`	High correlation
`casualty_home_area_type` is highly correlated with `accident_year`	High correlation
`casualty_class` is highly correlated with `accident_year` and 1 other fields	High correlation
`car_passenger` is highly correlated with `accident_year` and 1 other fields	High correlation
`vehicle_reference` is highly correlated with `casualty_reference`	High correlation
`casualty_reference` is highly correlated with `vehicle_reference`	High correlation
`casualty_class` is highly correlated with `pedestrian_location` and 2 other fields	High correlation
`age_of_casualty` is highly correlated with `age_band_of_casualty`	High correlation
`age_band_of_casualty` is highly correlated with `age_of_casualty`	High correlation
`pedestrian_location` is highly correlated with `casualty_class` and 1 other fields	High correlation
`pedestrian_movement` is highly correlated with `casualty_class` and 1 other fields	High correlation
`car_passenger` is highly correlated with `casualty_class`	High correlation
`casualty_home_area_type` is highly correlated with `casualty_imd_decile`	High correlation
`casualty_imd_decile` is highly correlated with `casualty_home_area_type`	High correlation
`vehicle_reference` is highly skewed (γ1 = 320.7263833)	Skewed
`casualty_reference` is highly skewed (γ1 = 224.095606)	Skewed
`accident_index` is uniformly distributed	Uniform
`accident_reference` is uniformly distributed	Uniform
`pedestrian_location` has 100834 (87.2%) zeros	Zeros
`pedestrian_movement` has 100833 (87.2%) zeros	Zeros
`bus_or_coach_passenger` has 114275 (98.9%) zeros	Zeros
`casualty_type` has 14750 (12.8%) zeros	Zeros

Reproduction

Analysis started	2022-02-22 14:08:06.803052
Analysis finished	2022-02-22 14:08:32.727717
Duration	25.92 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

accident_index
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	91199
Distinct (%)	78.9%
Missing	0
Missing (%)	0.0%
Memory size	903.1 KiB

2020440349165	41
2020990939366	19
2020140924772	17
2020460977371	13
2020470916576	12
Other values (91194)	115482

Length

Max length	13
Median length	13
Mean length	13
Min length	13

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	74161 ?
Unique (%)	64.2%

Sample

1st row	2020010219808
2nd row	2020010220496
3rd row	2020010220496
4th row	2020010228005
5th row	2020010228006

Common Values

Value	Count	Frequency (%)
2020440349165	41	< 0.1%
2020990939366	19	< 0.1%
2020140924772	17	< 0.1%
2020460977371	13	< 0.1%
2020470916576	12	< 0.1%
2020010237692	11	< 0.1%
2020170H10270	11	< 0.1%
2020350984206	11	< 0.1%
202006F172817	11	< 0.1%
2020160984754	10	< 0.1%
Other values (91189)	115428	99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2020440349165	41	< 0.1%
2020990939366	19	< 0.1%
2020140924772	17	< 0.1%
2020460977371	13	< 0.1%
2020470916576	12	< 0.1%
2020010237692	11	< 0.1%
2020170h10270	11	< 0.1%
2020350984206	11	< 0.1%
202006f172817	11	< 0.1%
2020160984754	10	< 0.1%
Other values (91189)	115428	99.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

accident_year
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	903.1 KiB

2020	115584

Length

Max length	4
Median length	4
Mean length	4
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2020
2nd row	2020
3rd row	2020
4th row	2020
5th row	2020

Common Values

Value	Count	Frequency (%)
2020	115584	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
2020	115584	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

accident_reference
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	91199
Distinct (%)	78.9%
Missing	0
Missing (%)	0.0%
Memory size	903.1 KiB

440349165	41
990939366	19
140924772	17
460977371	13
470916576	12
Other values (91194)	115482

Length

Max length	9
Median length	9
Mean length	9
Min length	9

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	74161 ?
Unique (%)	64.2%

Sample

1st row	010219808
2nd row	010220496
3rd row	010220496
4th row	010228005
5th row	010228006

Common Values

Value	Count	Frequency (%)
440349165	41	< 0.1%
990939366	19	< 0.1%
140924772	17	< 0.1%
460977371	13	< 0.1%
470916576	12	< 0.1%
010237692	11	< 0.1%
170H10270	11	< 0.1%
350984206	11	< 0.1%
06F172817	11	< 0.1%
160984754	10	< 0.1%
Other values (91189)	115428	99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
440349165	41	< 0.1%
990939366	19	< 0.1%
140924772	17	< 0.1%
460977371	13	< 0.1%
470916576	12	< 0.1%
010237692	11	< 0.1%
170h10270	11	< 0.1%
350984206	11	< 0.1%
06f172817	11	< 0.1%
160984754	10	< 0.1%
Other values (91189)	115428	99.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

vehicle_reference
Real number (ℝ_≥0)

HIGH CORRELATION
SKEWED

Distinct	12
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.460556824

Minimum	1
Maximum	999
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	903.1 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	1
Q3	2
95-th percentile	2
Maximum	999
Range	998
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	2.991765428
Coefficient of variation (CV)	2.048373181
Kurtosis	106936.656
Mean	1.460556824
Median Absolute Deviation (MAD)	0
Skewness	320.7263833
Sum	168817
Variance	8.950660379
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
1	67571	58.5%
2	44589	38.6%
3	2846	2.5%
4	436	0.4%
5	99	0.1%
6	25	< 0.1%
7	6	< 0.1%
8	5	< 0.1%
9	2	< 0.1%
10	2	< 0.1%
Other values (2)	3	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	67571	58.5%
2	44589	38.6%
3	2846	2.5%
4	436	0.4%
5	99	0.1%
6	25	< 0.1%
7	6	< 0.1%
8	5	< 0.1%
9	2	< 0.1%
10	2	< 0.1%

Value	Count	Frequency (%)
999	1	< 0.1%
11	2	< 0.1%
10	2	< 0.1%
9	2	< 0.1%
8	5	< 0.1%
7	6	< 0.1%
6	25	< 0.1%
5	99	0.1%
4	436	0.4%
3	2846	2.5%

casualty_reference
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct	43
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.347790352

Minimum	1
Maximum	992
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	903.1 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	1
Q3	1
95-th percentile	3
Maximum	992
Range	991
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	4.036720714
Coefficient of variation (CV)	2.995065745
Kurtosis	52821.99629
Mean	1.347790352
Median Absolute Deviation (MAD)	0
Skewness	224.095606
Sum	155783
Variance	16.29511412
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=43)

Value	Count	Frequency (%)
1	90228	78.1%
2	17657	15.3%
3	5020	4.3%
4	1689	1.5%
5	571	0.5%
6	203	0.2%
7	83	0.1%
8	35	< 0.1%
9	19	< 0.1%
10	13	< 0.1%
Other values (33)	66	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	90228	78.1%
2	17657	15.3%
3	5020	4.3%
4	1689	1.5%
5	571	0.5%
6	203	0.2%
7	83	0.1%
8	35	< 0.1%
9	19	< 0.1%
10	13	< 0.1%

Value	Count	Frequency (%)
992	1	< 0.1%
902	1	< 0.1%
41	1	< 0.1%
40	2	< 0.1%
39	1	< 0.1%
38	1	< 0.1%
37	1	< 0.1%
36	1	< 0.1%
35	1	< 0.1%
34	1	< 0.1%

casualty_class
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	903.1 KiB

1	79330
2	21504
3	14750

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	3
2nd row	3
3rd row	3
4th row	3
5th row	3

Common Values

Value	Count	Frequency (%)
1	79330	68.6%
2	21504	18.6%
3	14750	12.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	79330	68.6%
2	21504	18.6%
3	14750	12.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

sex_of_casualty
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	903.1 KiB

1	72335
2	42488
-1	756
9	5

Length

Max length	2
Median length	1
Mean length	1.006540698
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	2
3rd row	2
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	72335	62.6%
2	42488	36.8%
-1	756	0.7%
9	5	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	73091	63.2%
2	42488	36.8%
9	5	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

age_of_casualty
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	101
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	36.48974772

Minimum	-1
Maximum	99
Zeros	130
Zeros (%)	0.1%
Negative	2481
Negative (%)	2.1%
Memory size	903.1 KiB

Quantile statistics

Minimum	-1
5-th percentile	9
Q1	23
median	33
Q3	50
95-th percentile	72
Maximum	99
Range	100
Interquartile range (IQR)	27

Descriptive statistics

Standard deviation	18.98502214
Coefficient of variation (CV)	0.5202837324
Kurtosis	-0.2047937287
Mean	36.48974772
Median Absolute Deviation (MAD)	13
Skewness	0.4471627493
Sum	4217631
Variance	360.4310656
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
30	3135	2.7%
19	2881	2.5%
20	2796	2.4%
25	2749	2.4%
26	2739	2.4%
28	2738	2.4%
23	2718	2.4%
22	2718	2.4%
24	2699	2.3%
18	2686	2.3%
Other values (91)	87725	75.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-1	2481	2.1%
0	130	0.1%
1	186	0.2%
2	295	0.3%
3	349	0.3%
4	436	0.4%
5	436	0.4%
6	431	0.4%
7	529	0.5%
8	491	0.4%

Value	Count	Frequency (%)
99	2	< 0.1%
98	7	< 0.1%
97	2	< 0.1%
96	13	< 0.1%
95	21	< 0.1%
94	17	< 0.1%
93	38	< 0.1%
92	49	< 0.1%
91	66	0.1%
90	93	0.1%

age_band_of_casualty
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	12
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.292609704

Minimum	-1
Maximum	11
Zeros	0
Zeros (%)	0.0%
Negative	2481
Negative (%)	2.1%
Memory size	903.1 KiB

Quantile statistics

Minimum	-1
5-th percentile	2
Q1	5
median	6
Q3	8
95-th percentile	10
Maximum	11
Range	12
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.392856268
Coefficient of variation (CV)	0.3802645294
Kurtosis	0.6772990663
Mean	6.292609704
Median Absolute Deviation (MAD)	2
Skewness	-0.5028817423
Sum	727325
Variance	5.725761117
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
6	25511	22.1%
7	17805	15.4%
8	15669	13.6%
5	13568	11.7%
4	11627	10.1%
9	10390	9.0%
10	5337	4.6%
3	4740	4.1%
11	4025	3.5%
2	2599	2.2%
Other values (2)	4313	3.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-1	2481	2.1%
1	1832	1.6%
2	2599	2.2%
3	4740	4.1%
4	11627	10.1%
5	13568	11.7%
6	25511	22.1%
7	17805	15.4%
8	15669	13.6%
9	10390	9.0%

Value	Count	Frequency (%)
11	4025	3.5%
10	5337	4.6%
9	10390	9.0%
8	15669	13.6%
7	17805	15.4%
6	25511	22.1%
5	13568	11.7%
4	11627	10.1%
3	4740	4.1%
2	2599	2.2%

casualty_severity
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	903.1 KiB

3	94022
2	20102
1	1460

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	3
2nd row	3
3rd row	3
4th row	3
5th row	2

Common Values

Value	Count	Frequency (%)
3	94022	81.3%
2	20102	17.4%
1	1460	1.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
3	94022	81.3%
2	20102	17.4%
1	1460	1.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

pedestrian_location
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	12
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.6968611573

Minimum	-1
Maximum	10
Zeros	100834
Zeros (%)	87.2%
Negative	2
Negative (%)	< 0.1%
Memory size	903.1 KiB

Quantile statistics

Minimum	-1
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	5
Maximum	10
Range	11
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	2.059929866
Coefficient of variation (CV)	2.956011889
Kurtosis	8.474022246
Mean	0.6968611573
Median Absolute Deviation (MAD)	0
Skewness	3.047900729
Sum	80546
Variance	4.243311051
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
0	100834	87.2%
5	5828	5.0%
1	2419	2.1%
6	1709	1.5%
9	1614	1.4%
10	1393	1.2%
4	845	0.7%
8	757	0.7%
7	91	0.1%
2	70	0.1%
Other values (2)	24	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-1	2	< 0.1%
0	100834	87.2%
1	2419	2.1%
2	70	0.1%
3	22	< 0.1%
4	845	0.7%
5	5828	5.0%
6	1709	1.5%
7	91	0.1%
8	757	0.7%

Value	Count	Frequency (%)
10	1393	1.2%
9	1614	1.4%
8	757	0.7%
7	91	0.1%
6	1709	1.5%
5	5828	5.0%
4	845	0.7%
3	22	< 0.1%
2	70	0.1%
1	2419	2.1%

pedestrian_movement
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	11
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.5615915698

Minimum	-1
Maximum	9
Zeros	100833
Zeros (%)	87.2%
Negative	2
Negative (%)	< 0.1%
Memory size	903.1 KiB

Quantile statistics

Minimum	-1
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	4
Maximum	9
Range	10
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	1.879680074
Coefficient of variation (CV)	3.347058921
Kurtosis	13.20460614
Mean	0.5615915698
Median Absolute Deviation (MAD)	0
Skewness	3.749485504
Sum	64911
Variance	3.53319718
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	100833	87.2%
1	4593	4.0%
9	4132	3.6%
3	3093	2.7%
5	776	0.7%
2	746	0.6%
4	569	0.5%
8	417	0.4%
7	331	0.3%
6	92	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-1	2	< 0.1%
0	100833	87.2%
1	4593	4.0%
2	746	0.6%
3	3093	2.7%
4	569	0.5%
5	776	0.7%
6	92	0.1%
7	331	0.3%
8	417	0.4%

Value	Count	Frequency (%)
9	4132	3.6%
8	417	0.4%
7	331	0.3%
6	92	0.1%
5	776	0.7%
4	569	0.5%
3	3093	2.7%
2	746	0.6%
1	4593	4.0%
0	100833	87.2%

car_passenger
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	5
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	903.1 KiB

0	96655
1	11958
2	6543
-1	311
9	117

Length

Max length	2
Median length	1
Mean length	1.002690684
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	96655	83.6%
1	11958	10.3%
2	6543	5.7%
-1	311	0.3%
9	117	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	96655	83.6%
1	12269	10.6%
2	6543	5.7%
9	117	0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

bus_or_coach_passenger
Real number (ℝ)

ZEROS

Distinct	7
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.03895002769

Minimum	-1
Maximum	9
Zeros	114275
Zeros (%)	98.9%
Negative	22
Negative (%)	< 0.1%
Memory size	903.1 KiB

Quantile statistics

Minimum	-1
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	9
Range	10
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.3815276336
Coefficient of variation (CV)	9.795310974
Kurtosis	112.1954802
Mean	0.03895002769
Median Absolute Deviation (MAD)	0
Skewness	10.2245913
Sum	4502
Variance	0.1455633352
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	114275	98.9%
4	796	0.7%
3	350	0.3%
2	77	0.1%
1	55	< 0.1%
-1	22	< 0.1%
9	9	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-1	22	< 0.1%
0	114275	98.9%
1	55	< 0.1%
2	77	0.1%
3	350	0.3%
4	796	0.7%
9	9	< 0.1%

Value	Count	Frequency (%)
9	9	< 0.1%
4	796	0.7%
3	350	0.3%
2	77	0.1%
1	55	< 0.1%
0	114275	98.9%
-1	22	< 0.1%

pedestrian_road_maintenance_worker
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	903.1 KiB

0	114672
2	745
-1	94
1	73

Length

Max length	2
Median length	1
Mean length	1.000813261
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	114672	99.2%
2	745	0.6%
-1	94	0.1%
1	73	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	114672	99.2%
2	745	0.6%
1	167	0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

casualty_type
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	21
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.388366902

Minimum	0
Maximum	98
Zeros	14750
Zeros (%)	12.8%
Negative	0
Negative (%)	0.0%
Memory size	903.1 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	9
Q3	9
95-th percentile	11
Maximum	98
Range	98
Interquartile range (IQR)	8

Descriptive statistics

Standard deviation	9.914713615
Coefficient of variation (CV)	1.341935741
Kurtosis	56.13020795
Mean	7.388366902
Median Absolute Deviation (MAD)	0
Skewness	6.75436421
Sum	853977
Variance	98.30154606
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=21)

Value	Count	Frequency (%)
9	62698	54.2%
1	16294	14.1%
0	14750	12.8%
3	6993	6.1%
5	3677	3.2%
19	3235	2.8%
4	1546	1.3%
11	1506	1.3%
8	1419	1.2%
2	1001	0.9%
Other values (11)	2465	2.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	14750	12.8%
1	16294	14.1%
2	1001	0.9%
3	6993	6.1%
4	1546	1.3%
5	3677	3.2%
8	1419	1.2%
9	62698	54.2%
10	138	0.1%
11	1506	1.3%

Value	Count	Frequency (%)
98	209	0.2%
97	301	0.3%
90	695	0.6%
23	86	0.1%
22	155	0.1%
21	447	0.4%
20	263	0.2%
19	3235	2.8%
18	3	< 0.1%
17	82	0.1%

casualty_home_area_type
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	903.1 KiB

1	85122
3	10860
-1	10762
2	8840

Length

Max length	2
Median length	1
Mean length	1.093109773
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	85122	73.6%
3	10860	9.4%
-1	10762	9.3%
2	8840	7.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	95884	83.0%
3	10860	9.4%
2	8840	7.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

casualty_imd_decile
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	11
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.36113995

Minimum	-1
Maximum	10
Zeros	0
Zeros (%)	0.0%
Negative	10910
Negative (%)	9.4%
Memory size	903.1 KiB

Quantile statistics

Minimum	-1
5-th percentile	-1
Q1	2
median	4
Q3	7
95-th percentile	10
Maximum	10
Range	11
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	3.171409748
Coefficient of variation (CV)	0.7271974263
Kurtosis	-0.9571051199
Mean	4.36113995
Median Absolute Deviation (MAD)	2
Skewness	0.08002709433
Sum	504078
Variance	10.05783979
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
2	13604	11.8%
1	12848	11.1%
3	12826	11.1%
4	11597	10.0%
-1	10910	9.4%
5	10863	9.4%
6	10225	8.8%
7	9255	8.0%
8	8438	7.3%
9	8068	7.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-1	10910	9.4%
1	12848	11.1%
2	13604	11.8%
3	12826	11.1%
4	11597	10.0%
5	10863	9.4%
6	10225	8.8%
7	9255	8.0%
8	8438	7.3%
9	8068	7.0%

Value	Count	Frequency (%)
10	6950	6.0%
9	8068	7.0%
8	8438	7.3%
7	9255	8.0%
6	10225	8.8%
5	10863	9.4%
4	11597	10.0%
3	12826	11.1%
2	13604	11.8%
1	12848	11.1%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	accident_index	accident_year	accident_reference	vehicle_reference	casualty_reference	casualty_class	sex_of_casualty	age_of_casualty	age_band_of_casualty	casualty_severity	pedestrian_location	pedestrian_movement	casualty_type	casualty_home_area_type	casualty_imd_decile
0	2020010219808	2020	010219808	1	1	3	1	31	6	3	9	5	0	1	4
1	2020010220496	2020	010220496	1	1	3	2	2	1	3	1	1	0	1	2
2	2020010220496	2020	010220496	1	2	3	2	4	1	3	1	1	0	1	2
3	2020010228005	2020	010228005	1	1	3	1	23	5	3	5	9	0	1	3
4	2020010228006	2020	010228006	1	1	3	1	47	8	2	4	1	0	1	3
5	2020010228011	2020	010228011	1	1	3	2	32	6	3	6	9	0	1	8
6	2020010228011	2020	010228011	1	2	3	2	33	6	3	6	9	0	-1	-1
7	2020010228012	2020	010228012	1	1	1	1	25	5	3	0	0	9	1	4
8	2020010228014	2020	010228014	1	1	1	1	41	7	3	0	0	9	1	3
9	2020010228017	2020	010228017	1	1	3	1	50	8	2	9	9	0	1	3

Last rows

	accident_index	accident_year	accident_reference	vehicle_reference	casualty_reference	casualty_class	sex_of_casualty	age_of_casualty	age_band_of_casualty	casualty_severity	pedestrian_location	pedestrian_movement	car_passenger	casualty_type	casualty_home_area_type	casualty_imd_decile
115574	2020991023880	2020	991023880	1	1	3	2	58	9	3	5	1	0	0	1	4
115575	2020991024039	2020	991024039	2	1	1	1	52	8	3	0	0	0	9	3	4
115576	2020991024209	2020	991024209	1	1	1	2	33	6	3	0	0	0	9	1	10
115577	2020991024209	2020	991024209	1	2	2	2	13	3	3	0	0	2	9	1	8
115578	2020991024526	2020	991024526	1	1	3	1	69	10	3	6	9	0	0	3	7
115579	2020991027064	2020	991027064	2	1	1	1	11	3	2	0	0	0	1	1	2
115580	2020991029573	2020	991029573	1	1	3	2	63	9	3	10	1	0	0	1	10
115581	2020991030297	2020	991030297	2	1	1	1	38	7	2	0	0	0	5	2	9
115582	2020991030900	2020	991030900	2	1	1	1	76	11	3	0	0	0	1	1	9
115583	2020991032575	2020	991032575	1	1	3	1	48	8	3	9	9	0	0	1	1

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block